Evidence map›Paper›PMID 31879854›Full record

ArticleBrain topography2020

Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition.

Guanghui Zhang, Chi Zhang, Shuo Cao, Xue Xia, Xin Tan, Lichengxi Si, Chenxin Wang, Xiaochun Wang, Chenglin Zhou, Tapani Ristaniemi and 1 more

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Article in Brain topography, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Guanghui ZhangSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.ORCID 0000-0003-0134-3614
Chi ZhangSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.
Shuo CaoSchool of Foreign Languages, Dalian University of Technology, Dalian, 116024, China.
Xue XiaSchool of Psychology, Shanghai University of Sport, Shanghai, 200438, China.
Xin TanSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.
Lichengxi SiSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.
Chenxin WangSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.
Xiaochun WangSchool of Psychology, Shanghai University of Sport, Shanghai, 200438, China.
Chenglin ZhouSchool of Psychology, Shanghai University of Sport, Shanghai, 200438, China.
Tapani RistaniemiFaculty of Information Technology, University of Jyväskylä, 40100, Jyvaskyla, Finland.
Fengyu CongSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. cong@dlut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time-frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP data) influences the time-frequency results. In this study, an optimal set of CFBW was firstly selected from the number sets of CFBW, to further analyze for TFA of the ERP data in a cognitive experiment paradigm of emotion (Anger and Neutral) and task (Go and Nogo). Then tensor decomposition algorithm was introduced to investigate the NPLC of interest from the fourth-order tensor. Compared with the TFA results which only revealed a significant difference between Go and Nogo task condition, the tensor-based analysis showed significant interaction effect between emotion and task. Moreover, significant differences were found in both emotion and task conditions through tensor decomposition. In addition, the statistical results of TFA would be affected by the selected region of interest (ROI), whereas those of the proposed method were not subject to ROI. Hence, this study demonstrated that tensor decomposition method was effective in extracting NPLC, by considering spatial information simultaneously as the potential to explore the brain mechanisms related to experimental design.

Indexed as

AlgorithmsBrainElectroencephalographyEmotionsEvoked PotentialsHumansMaleYoung AdultERPMother waveletNon-phase lockedTensor decompositionTime–frequency analysis

Identifiers

PMID31879854
PMCPMC6943407

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